StreamPref: a query language for temporal conditional preferences on data streams

2019 ◽  
Vol 53 (2) ◽  
pp. 329-360
Author(s):  
Marcos Roberto Ribeiro ◽  
Maria Camila N. Barioni ◽  
Sandra de Amo ◽  
Claudia Roncancio ◽  
Cyril Labbé
Keyword(s):  
Data Streams ◽  
Query Language ◽  
Conditional Preferences

Composite Event Processing for Data Streams and Domain Knowledge

2011 ◽  
Vol 219-220 ◽  
pp. 927-931
Author(s):  
Jun Qiang Liu ◽  
Xiao Ling Guan
Keyword(s):  
Query Optimization ◽  
Data Streams ◽  
Domain Knowledge ◽  
Semantic Information ◽  
Query Language ◽  
Processing System ◽  
Optimization Techniques ◽  
Research Attention ◽  
Composite Event ◽  
Solid Foundation

In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possible to express queries with a sound semantics, but also provides a solid foundation for query optimization. Experiment results show that our approach is effective and efficient for data streams and domain knowledge.

PROBABILISTIC QUERYING OVER UNCERTAIN DATA STREAMS

2012 ◽  
Vol 20 (05) ◽  
pp. 701-728 ◽  
Author(s):  
MOHAMMAD G. DEZFULI ◽  
MOSTAFA S. HAGHJOO
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Memory Management ◽  
Query Language ◽  
Uncertain Data ◽  
Sensor Data ◽  
Data Stream Management ◽  
Probabilistic Data ◽  
Stream Management ◽  
Probabilistic Data Streams

Inherent imprecision of data in many applications motivates us to support uncertainty as a first-class concept. Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor data management systems and object monitoring systems which need both issues in tandem. Our main contribution is designing a probabilistic data stream management system, called Sarcheshmeh, for continuous querying over probabilistic data streams. Sarcheshmeh supports uncertainty from input data to final query results. In this paper, after reviewing requirements and applications of probabilistic data streams, we present our new data model for probabilistic data streams and define our main logical operators formally. Then, we present query language and physical operators. In addition, we introduce the architecture of Sarcheshmeh and also describe some major challenges like memory management and our floating precision mechanism toward designing a more robust system. Finally, we report evaluation of our system and the effect of floating precision on the tradeoff between accuracy and efficiency.

scholarly journals A oneM2M-Based Query Engine for Internet of Things (IoT) Data Streams

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3253 ◽  
Author(s):  
Putu Widya ◽  
Yoga Yustiawan ◽  
Joonho Kwon
Keyword(s):  
Internet Of Things ◽  
Query Processing ◽  
Data Streams ◽  
Query Language ◽  
Data Set ◽  
Real Time Processing ◽  
Network Bandwidth ◽  
Query Engine ◽  
Processing Algorithms ◽  
User Queries

The new standard oneM2M (one machine-to-machine) aims to standardize the architecture and protocols of Internet of Things (IoT) middleware for better interoperability. Although the standard seems promising, it lacks several features for efficiently searching and retrieving IoT data which satisfy users’ intentions. In this paper, we design and develop a oneM2M-based query engine, called OMQ, that provides a real-time processing over IoT data streams. For this purpose, we define a query language which enables users to retrieve IoT data from data sources using JavaScript Object Notation (JSON). We also propose efficient query processing algorithms which utilizes the oneM2M architecture consisting of two nodes: (1) the IoT node and (2) the infrastructure node. IoT nodes of OMQ are mainly sensor devices execute user queries the aggregate, transform and filter operators, whereas the infrastructure node handles the join operator of user queries. Since the query processing algorithms are implemented as the hybrid infrastructure-edge processing, user queries can be executed efficiently in each IoT node rather than only in the infrastructure node. Thus, our OMQ system reduces the query processing time and the network bandwidth. We conducted a comprehensive evaluation of OMQ using a real and a synthetic data set. Experimental results demonstrate the feasibility and efficiency of OMQ system for executing queries and transferring data from each IoT node.

scholarly journals C-SPARQL: A CONTINUOUS QUERY LANGUAGE FOR RDF DATA STREAMS

2010 ◽  
Vol 04 (01) ◽  
pp. 3-25 ◽  
Author(s):  
DAVIDE FRANCESCO BARBIERI ◽  
DANIELE BRAGA ◽  
STEFANO CERI ◽  
EMANUELE DELLA VALLE ◽  
MICHAEL GROSSNIKLAUS
Keyword(s):  
Data Streams ◽  
Query Language ◽  
Formal Semantics ◽  
Streaming Data ◽  
Continuous Queries ◽  
Continuous Query ◽  
Urban Computing ◽  
Rdf Data ◽  
Over Time

This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time. C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

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